{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "afd55886-5f5b-4794-838e-ef8179fb0394",
   "metadata": {},
   "source": [
    "##### **** These pip installs need to be adapted to use the appropriate release level. Alternatively, The venv running the jupyter lab could be pre-configured with a requirement file that includes the right release. Example for transform developers working from git clone:\n",
    "```\n",
    "make venv \n",
    "source venv/bin/activate \n",
    "pip install jupyterlab\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c45c3c6-e4d7-4e61-8de6-32d61f2ce695",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "## This is here as a reference only. \n",
    "#%pip install data-prep-toolkit\n",
    "#%pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf1f782-0e61-485c-8670-81066beb734c",
   "metadata": {},
   "source": [
    "##### ***** Import required classes and modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2a12abc-9460-4e45-8961-873b48a9ab19",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dpk_people import People"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7234563c-2924-4150-8a31-4aec98c1bf33",
   "metadata": {},
   "source": [
    "##### ***** Setup runtime parameters for this transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e90a853e-412f-45d7-af3d-959e755aeebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "People(\n",
    "    input_folder='test-data/input',\n",
    "    output_folder='output'\n",
    ").transform()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3df5adf-4717-4a03-864d-9151cd3f134b",
   "metadata": {},
   "source": [
    "##### **** The specified folder will include the transformed parquet files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7276fe84-6512-4605-ab65-747351e13a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "glob.glob(\"output/*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "845a75cf-f4a9-467d-87fa-ccbac1c9beb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyarrow.parquet as pq\n",
    "import pandas as pd\n",
    "\n",
    "# Read the Parquet file into an Arrow Table\n",
    "df = pq.read_table('output/test_0.parquet').to_pandas()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "569ad11f-8873-4b97-bb31-0fb2495583c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "from IPython.display import display\n",
    "import PIL.Image as Image\n",
    "    \n",
    "image = Image.open(io.BytesIO(df.iloc[0]['blurred_images'][0]))\n",
    "display(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5e414ec-0853-4881-aee8-3efbb8a5218a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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